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     "text": [
      "/kaggle/input/learn-ai-bbc/BBC News Train.csv\n",
      "/kaggle/input/learn-ai-bbc/BBC News Sample Solution.csv\n",
      "/kaggle/input/learn-ai-bbc/BBC News Test.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score, classification_report, accuracy_score\n",
    "plt.style.use('ggplot')\n",
    "from sklearn.metrics import rand_score,mutual_info_score,jaccard_score,fowlkes_mallows_score\n",
    "\n",
    "#nlp related\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.util import ngrams\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import re\n",
    "import string\n",
    "from sklearn import feature_extraction, linear_model, model_selection, preprocessing\n",
    "\n",
    "\n",
    "from collections import  Counter\n",
    "from collections import defaultdict\n",
    "\n",
    "np.random.seed(42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47a25f28",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set shape:  (1490, 3)\n",
      "test set shape: (735, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ArticleId</th>\n",
       "      <th>Text</th>\n",
       "      <th>Category</th>\n",
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      ],
      "text/plain": [
       "   ArticleId                                               Text  Category\n",
       "0       1833  worldcom ex-boss launches defence lawyers defe...  business\n",
       "1        154  german business confidence slides german busin...  business\n",
       "2       1101  bbc poll indicates economic gloom citizens in ...  business\n",
       "3       1976  lifestyle  governs mobile choice  faster  bett...      tech\n",
       "4        917  enron bosses in $168m payout eighteen former e...  business"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data using pandas : Create a DataFrame named df, that contains the training data \n",
    "df = pd.read_csv('../input/learn-ai-bbc/BBC News Train.csv')\n",
    "df_submit = pd.read_csv('../input/learn-ai-bbc/BBC News Test.csv')\n",
    "print('train set shape: ', df.shape)\n",
    "print('test set shape:', df_submit.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a2cf9e",
   "metadata": {
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    "tags": []
   },
   "source": [
    "### Step 1 \n",
    "Extracting word features and show Exploratory Data Analysis (EDA) — Inspect, Visualize and Clean the Data"
   ]
  },
  {
   "cell_type": "code",
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    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>Category</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>1976</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>1485</th>\n",
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       "      <th>1486</th>\n",
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       "      <td>dj double act revamp chart show dj duo jk and ...</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>1590</td>\n",
       "      <td>weak dollar hits reuters revenues at media gro...</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>1587</td>\n",
       "      <td>apple ipod family expands market apple has exp...</td>\n",
       "      <td>tech</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>538</td>\n",
       "      <td>santy worm makes unwelcome visit thousands of ...</td>\n",
       "      <td>tech</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1490 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ArticleId                                               Text  \\\n",
       "0          1833  worldcom ex-boss launches defence lawyers defe...   \n",
       "1           154  german business confidence slides german busin...   \n",
       "2          1101  bbc poll indicates economic gloom citizens in ...   \n",
       "3          1976  lifestyle  governs mobile choice  faster  bett...   \n",
       "4           917  enron bosses in $168m payout eighteen former e...   \n",
       "...         ...                                                ...   \n",
       "1485        857  double eviction from big brother model caprice...   \n",
       "1486        325  dj double act revamp chart show dj duo jk and ...   \n",
       "1487       1590  weak dollar hits reuters revenues at media gro...   \n",
       "1488       1587  apple ipod family expands market apple has exp...   \n",
       "1489        538  santy worm makes unwelcome visit thousands of ...   \n",
       "\n",
       "           Category  target  \n",
       "0          business       0  \n",
       "1          business       0  \n",
       "2          business       0  \n",
       "3              tech       4  \n",
       "4          business       0  \n",
       "...             ...     ...  \n",
       "1485  entertainment       1  \n",
       "1486  entertainment       1  \n",
       "1487       business       0  \n",
       "1488           tech       4  \n",
       "1489           tech       4  \n",
       "\n",
       "[1490 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df['target'] = df['Category'].astype('category').cat.codes\n",
    "id_category_mapping = {\n",
    "  \"business\" : 0,\n",
    "  \"entertainment\" : 1,\n",
    "  \"politics\" : 2,\n",
    "  \"sport\" : 3,\n",
    "  \"tech\" : 4\n",
    "}\n",
    "df[\"target\"] = df[\"Category\"].map(id_category_mapping)\n",
    "df"
   ]
  },
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sport            346\n",
      "business         336\n",
      "politics         274\n",
      "entertainment    273\n",
      "tech             261\n",
      "Name: Category, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Class type')"
      ]
     },
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = df.Category.value_counts()\n",
    "sns.barplot(x=count.index, y=count) #, color='#CFB87C'\n",
    "print(count)\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Class type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01ef74e",
   "metadata": {
    "papermill": {
     "duration": 0.037099,
     "end_time": "2022-04-13T02:02:02.171812",
     "exception": false,
     "start_time": "2022-04-13T02:02:02.134713",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Text cleaning common practice\n",
    "1. Removing/Retaining Stopwords\n",
    "2. Lemmatizing/Stemming\n",
    "3. Spell correction\n",
    "4. Removing special symbols like urls, emoji, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c24f9a5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:02.254885Z",
     "iopub.status.busy": "2022-04-13T02:02:02.254072Z",
     "iopub.status.idle": "2022-04-13T02:02:02.586735Z",
     "shell.execute_reply": "2022-04-13T02:02:02.586179Z",
     "shell.execute_reply.started": "2022-04-13T01:07:27.887360Z"
    },
    "papermill": {
     "duration": 0.377512,
     "end_time": "2022-04-13T02:02:02.586881",
     "exception": false,
     "start_time": "2022-04-13T02:02:02.209369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ArticleId</th>\n",
       "      <th>Text</th>\n",
       "      <th>Category</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1833</td>\n",
       "      <td>worldcom ex  boss launches defence lawyers def...</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>154</td>\n",
       "      <td>german business confidence slides german busin...</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1101</td>\n",
       "      <td>bbc poll indicates economic gloom citizens in ...</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976</td>\n",
       "      <td>lifestyle  governs mobile choice  faster  bett...</td>\n",
       "      <td>tech</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>917</td>\n",
       "      <td>enron bosses in   168m payout eighteen former ...</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>857</td>\n",
       "      <td>double eviction from big brother model caprice...</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>325</td>\n",
       "      <td>dj double act revamp chart show dj duo jk and ...</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>1590</td>\n",
       "      <td>weak dollar hits reuters revenues at media gro...</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>1587</td>\n",
       "      <td>apple ipod family expands market apple has exp...</td>\n",
       "      <td>tech</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>538</td>\n",
       "      <td>santy worm makes unwelcome visit thousands of ...</td>\n",
       "      <td>tech</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1490 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ArticleId                                               Text  \\\n",
       "0          1833  worldcom ex  boss launches defence lawyers def...   \n",
       "1           154  german business confidence slides german busin...   \n",
       "2          1101  bbc poll indicates economic gloom citizens in ...   \n",
       "3          1976  lifestyle  governs mobile choice  faster  bett...   \n",
       "4           917  enron bosses in   168m payout eighteen former ...   \n",
       "...         ...                                                ...   \n",
       "1485        857  double eviction from big brother model caprice...   \n",
       "1486        325  dj double act revamp chart show dj duo jk and ...   \n",
       "1487       1590  weak dollar hits reuters revenues at media gro...   \n",
       "1488       1587  apple ipod family expands market apple has exp...   \n",
       "1489        538  santy worm makes unwelcome visit thousands of ...   \n",
       "\n",
       "           Category  target  \n",
       "0          business       0  \n",
       "1          business       0  \n",
       "2          business       0  \n",
       "3              tech       4  \n",
       "4          business       0  \n",
       "...             ...     ...  \n",
       "1485  entertainment       1  \n",
       "1486  entertainment       1  \n",
       "1487       business       0  \n",
       "1488           tech       4  \n",
       "1489           tech       4  \n",
       "\n",
       "[1490 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_text(text):\n",
    "    # punctuations and special characters\n",
    "    punctuations = '@#!?+&*[]-%.:/();$£￥=><|{}^' + \"'`\"\n",
    "    for p in punctuations:\n",
    "        text = text.replace(p, f' {p} ')\n",
    "    \n",
    "    #\n",
    "    table = str.maketrans('','',string.punctuation)\n",
    "    text = text.translate(table)\n",
    "        \n",
    "        \n",
    "    return text\n",
    "\n",
    "df['Text'] = df['Text'].apply(lambda s : clean_text(s))\n",
    "df_submit['Text'] = df_submit['Text'].apply(lambda s : clean_text(s))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc9b9e7",
   "metadata": {
    "papermill": {
     "duration": 0.037495,
     "end_time": "2022-04-13T02:02:02.662242",
     "exception": false,
     "start_time": "2022-04-13T02:02:02.624747",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Creat corpus, a list contain all words in datasets. Then count and plot most common stop words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7a71709",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:02.748018Z",
     "iopub.status.busy": "2022-04-13T02:02:02.747295Z",
     "iopub.status.idle": "2022-04-13T02:02:02.993632Z",
     "shell.execute_reply": "2022-04-13T02:02:02.994254Z",
     "shell.execute_reply.started": "2022-04-13T01:07:28.234513Z"
    },
    "papermill": {
     "duration": 0.293381,
     "end_time": "2022-04-13T02:02:02.994440",
     "exception": false,
     "start_time": "2022-04-13T02:02:02.701059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_corpus(category, df):\n",
    "    corpus=[]\n",
    "    \n",
    "    if category is None:\n",
    "        for x in df['Text'].str.split():\n",
    "            for i in x:\n",
    "                corpus.append(i)\n",
    "        \n",
    "    else:\n",
    "        for x in df[df['Category']==category]['Text'].str.split():\n",
    "            for i in x:\n",
    "                corpus.append(i)\n",
    "            \n",
    "    return corpus\n",
    "\n",
    "\n",
    "all_corpus = create_corpus(None, df)\n",
    "cor_bu = create_corpus('business', df)\n",
    "cor_tech = create_corpus('tech', df)\n",
    "cor_pol = create_corpus('politics', df)\n",
    "cor_sport = create_corpus('sport', df)\n",
    "cor_ent = create_corpus('entertainment', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb0accb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:03.096957Z",
     "iopub.status.busy": "2022-04-13T02:02:03.075908Z",
     "iopub.status.idle": "2022-04-13T02:02:03.321980Z",
     "shell.execute_reply": "2022-04-13T02:02:03.322633Z",
     "shell.execute_reply.started": "2022-04-13T01:07:28.531401Z"
    },
    "papermill": {
     "duration": 0.289492,
     "end_time": "2022-04-13T02:02:03.322843",
     "exception": false,
     "start_time": "2022-04-13T02:02:03.033351",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop=set(stopwords.words('english'))\n",
    "\n",
    "counter = Counter(all_corpus)\n",
    "most = counter.most_common()\n",
    "x_0, y_0 = [], []\n",
    "for word,count in most[:80]:\n",
    "    if (word not in stop):\n",
    "        x_0.append(word)\n",
    "        y_0.append(count)\n",
    "\n",
    "counter = Counter(cor_bu)\n",
    "most = counter.most_common()\n",
    "x_1, y_1 = [], []\n",
    "for word,count in most[:60]:\n",
    "    if (word not in stop) :\n",
    "        x_1.append(word)\n",
    "        y_1.append(count)\n",
    "\n",
    "counter = Counter(cor_tech)\n",
    "most = counter.most_common()\n",
    "x_tech, y_tech = [], []\n",
    "for word,count in most[:60]:\n",
    "    if (word not in stop) :\n",
    "        x_tech.append(word)\n",
    "        y_tech.append(count)\n",
    "\n",
    "        \n",
    "counter = Counter(cor_pol)\n",
    "most = counter.most_common()\n",
    "x_pol, y_pol = [], []\n",
    "for word,count in most[:60]:\n",
    "    if (word not in stop) :\n",
    "        x_pol.append(word)\n",
    "        y_pol.append(count)\n",
    "        \n",
    "counter = Counter(cor_sport)\n",
    "most = counter.most_common()\n",
    "x_4, y_4 = [], []\n",
    "for word,count in most[:60]:\n",
    "    if (word not in stop) :\n",
    "        x_4.append(word)\n",
    "        y_4.append(count)\n",
    "        \n",
    "counter = Counter(cor_ent)\n",
    "most = counter.most_common()\n",
    "x_5, y_5 = [], []\n",
    "for word,count in most[:60]:\n",
    "    if (word not in stop) :\n",
    "        x_5.append(word)\n",
    "        y_5.append(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2879ce82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:03.402983Z",
     "iopub.status.busy": "2022-04-13T02:02:03.402312Z",
     "iopub.status.idle": "2022-04-13T02:02:05.042671Z",
     "shell.execute_reply": "2022-04-13T02:02:05.043197Z",
     "shell.execute_reply.started": "2022-04-13T01:07:28.803254Z"
    },
    "papermill": {
     "duration": 1.681432,
     "end_time": "2022-04-13T02:02:05.043373",
     "exception": false,
     "start_time": "2022-04-13T02:02:03.361941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2304 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f, axes = plt.subplots(6, 1, figsize=(10,32))\n",
    "sns.barplot(ax=axes[0], x=y_0,y=x_0)\n",
    "axes[0].set_title('Most common words in all articles')\n",
    "\n",
    "sns.barplot(ax=axes[1], x=y_1,y=x_1)\n",
    "axes[1].set_title('Most common words in business articles')\n",
    "\n",
    "sns.barplot(ax=axes[2], x=y_tech,y=x_tech)\n",
    "axes[2].set_title(\"Most common words in tech articles\")\n",
    "\n",
    "sns.barplot(ax=axes[3], x=y_pol,y=x_pol)\n",
    "axes[3].set_title(\"Most common words in pol articles\")\n",
    "\n",
    "sns.barplot(ax=axes[4], x=y_4,y=x_4)\n",
    "axes[4].set_title(\"Most common words in sport articles\")\n",
    "\n",
    "sns.barplot(ax=axes[5], x=y_5,y=x_5)\n",
    "axes[5].set_title(\"Most common words in entertainment articles\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219af424",
   "metadata": {
    "papermill": {
     "duration": 0.044029,
     "end_time": "2022-04-13T02:02:05.131082",
     "exception": false,
     "start_time": "2022-04-13T02:02:05.087053",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f36f16",
   "metadata": {
    "papermill": {
     "duration": 0.041393,
     "end_time": "2022-04-13T02:02:05.214688",
     "exception": false,
     "start_time": "2022-04-13T02:02:05.173295",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Build word vectors\n",
    "1. Count vectorizer and TF-IDF were used.\n",
    "2. count vector: matrix columns are words appear in all text, and each row indicate article id i contain certain words, if contain the word the row is 1, otherwise 0.\n",
    "\n",
    "2. TF-IDF: This method give vectors importance of related words in document/sentence, unlike bag of words. e.g If a word appear in sentence a lot, TF-IDF will treat it as a common word(a,like, said etc.) and reduce its importance. Here are the formula.\n",
    "\n",
    "TF=term frequency= number of word appear in sentence / number of word in sentence\n",
    "\n",
    "IDF = inverse document frequence = log ( number of sentences in doc / number of sentences contain the word)\n",
    "\n",
    "3. For the result: TF_IDF is slightly better than count vector, in terms of rand score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d447fd90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:05.302329Z",
     "iopub.status.busy": "2022-04-13T02:02:05.301287Z",
     "iopub.status.idle": "2022-04-13T02:02:07.876525Z",
     "shell.execute_reply": "2022-04-13T02:02:07.875215Z",
     "shell.execute_reply.started": "2022-04-13T01:07:30.523940Z"
    },
    "papermill": {
     "duration": 2.620629,
     "end_time": "2022-04-13T02:02:07.876818",
     "exception": false,
     "start_time": "2022-04-13T02:02:05.256189",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1490, 7115)\n",
      "(1490, 9927)\n",
      "(735, 5306)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "#count vector\n",
    "vectorizer = CountVectorizer(min_df=5, max_df=0.9, stop_words='english', \n",
    "                            lowercase=True)\n",
    "count_vec = vectorizer.fit_transform(df.Text)\n",
    "print(count_vec.shape)\n",
    "\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', \n",
    "                        ngram_range=(1,2),stop_words='english')  #unigrams and bigrams\n",
    "\n",
    "#tfidf = TfidfVectorizer(stop_words='english')\n",
    "tfidf_vector = tfidf.fit_transform(df.Text)\n",
    "tfidf_vector_sub = tfidf.fit_transform(df_submit.Text).toarray()\n",
    "\n",
    "#tfidf_vectorizer = TfidfVectorizer()\n",
    "#X_train_tf = tfidf_vectorizer.fit_transform(X_train.values)\n",
    "#X_test_tf = tfidf_vectorizer.fit_transform(X_test)\n",
    "#submit_tf = tfidf_vectorizer.fit_transform(df_submit['Text'])\n",
    "\n",
    "print(tfidf_vector.shape)\n",
    "print(tfidf_vector_sub.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "604fdb21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:07.971930Z",
     "iopub.status.busy": "2022-04-13T02:02:07.971065Z",
     "iopub.status.idle": "2022-04-13T02:02:07.978437Z",
     "shell.execute_reply": "2022-04-13T02:02:07.977853Z",
     "shell.execute_reply.started": "2022-04-13T01:07:33.211240Z"
    },
    "papermill": {
     "duration": 0.05642,
     "end_time": "2022-04-13T02:02:07.978601",
     "exception": false,
     "start_time": "2022-04-13T02:02:07.922181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1192, 9927)\n",
      "(298, 9927)\n",
      "(298,)\n"
     ]
    }
   ],
   "source": [
    "#vectorizer text \n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(\n",
    "#     count_vec, df['target'].values, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    tfidf_vector, df['target'].values, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4082d13d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:08.073781Z",
     "iopub.status.busy": "2022-04-13T02:02:08.072939Z",
     "iopub.status.idle": "2022-04-13T02:02:15.813323Z",
     "shell.execute_reply": "2022-04-13T02:02:15.814422Z",
     "shell.execute_reply.started": "2022-04-13T01:07:33.223458Z"
    },
    "papermill": {
     "duration": 7.792382,
     "end_time": "2022-04-13T02:02:15.814661",
     "exception": false,
     "start_time": "2022-04-13T02:02:08.022279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  FutureWarning,\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1341, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "np.random.seed(10)\n",
    "\n",
    "SAMPLE_SIZE = int(tfidf_vector.shape[0] * 0.9)\n",
    "indices = np.random.choice(range(tfidf_vector.shape[0]), size=SAMPLE_SIZE, replace=False)         \n",
    "projected_features = TSNE(n_components=2, random_state=0).fit_transform(tfidf_vector[indices]) \n",
    "projected_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "271086ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:15.915879Z",
     "iopub.status.busy": "2022-04-13T02:02:15.915077Z",
     "iopub.status.idle": "2022-04-13T02:02:16.274632Z",
     "shell.execute_reply": "2022-04-13T02:02:16.275151Z",
     "shell.execute_reply.started": "2022-04-13T01:07:40.414424Z"
    },
    "papermill": {
     "duration": 0.414681,
     "end_time": "2022-04-13T02:02:16.275339",
     "exception": false,
     "start_time": "2022-04-13T02:02:15.860658",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for category, category_id in sorted(id_category_mapping.items()):\n",
    "    points = projected_features[(df.target[indices] == category_id).values]\n",
    "    plt.scatter(points[:, 0], points[:, 1], s=20, label=category)\n",
    "\n",
    "plt.title(\"tf-idf embedding for 5 types of articel\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3580d711",
   "metadata": {
    "papermill": {
     "duration": 0.0462,
     "end_time": "2022-04-13T02:02:16.368307",
     "exception": false,
     "start_time": "2022-04-13T02:02:16.322107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7d4e28",
   "metadata": {
    "papermill": {
     "duration": 0.050713,
     "end_time": "2022-04-13T02:02:16.465395",
     "exception": false,
     "start_time": "2022-04-13T02:02:16.414682",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Step 2\n",
    "\n",
    "Building and training models\n",
    "\n",
    "**Some thoughts:**\n",
    "1. Since matrix factorization is consider as unsupersived method, durning training we donnot need to worry about model getting the testset label info, in theory, either  include text or not as input matrix is fine for this problem. And I have tried both, the result is without test matrix the rand score is slightly higher(98.1% vs 97.3%).\n",
    "2. Building model using NMF, set n_components=5, and tf-idf matrix is more efficient with KL loss. \n",
    "3. For measuring performances I use 3 metrics, accuarccy, rand score and fowlkes_mallows_score, the later 2 are better for clusting problem, cause we assume doing unsupervise learning, it's without labels.\n",
    "4. Display score as result.\n",
    "5. Improving by fine tunning the model, changing train/test size."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ac0dd9",
   "metadata": {
    "papermill": {
     "duration": 0.045293,
     "end_time": "2022-04-13T02:02:16.557492",
     "exception": false,
     "start_time": "2022-04-13T02:02:16.512199",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "##### Non-Negative Matrix Factorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d029b82b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:16.655416Z",
     "iopub.status.busy": "2022-04-13T02:02:16.654660Z",
     "iopub.status.idle": "2022-04-13T02:02:18.245745Z",
     "shell.execute_reply": "2022-04-13T02:02:18.246669Z",
     "shell.execute_reply.started": "2022-04-13T01:07:40.775947Z"
    },
    "papermill": {
     "duration": 1.644085,
     "end_time": "2022-04-13T02:02:18.246967",
     "exception": false,
     "start_time": "2022-04-13T02:02:16.602882",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:294: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.13 s, sys: 1.11 s, total: 3.24 s\n",
      "Wall time: 1.58 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NMF(beta_loss='kullback-leibler', max_iter=500, n_components=5, random_state=10,\n",
       "    solver='mu')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "from sklearn.decomposition import NMF\n",
    "\n",
    "nmf = NMF(n_components=5, max_iter=500,solver='mu',\n",
    "          beta_loss='kullback-leibler',random_state=10) \n",
    "\n",
    "#nmf.fit(tfidf_vector)\n",
    "nmf.fit(X_train)\n",
    "#H = nmf.components_\n",
    "\n",
    "# print('H',H.shape)\n",
    "# print('W', W.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ff9fe358",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:18.397536Z",
     "iopub.status.busy": "2022-04-13T02:02:18.396635Z",
     "iopub.status.idle": "2022-04-13T02:02:18.630723Z",
     "shell.execute_reply": "2022-04-13T02:02:18.632377Z",
     "shell.execute_reply.started": "2022-04-13T01:07:42.299226Z"
    },
    "papermill": {
     "duration": 0.297323,
     "end_time": "2022-04-13T02:02:18.632761",
     "exception": false,
     "start_time": "2022-04-13T02:02:18.335438",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.18456375838926176\n",
      "rand score 0.9810634307278603\n",
      "fm score 0.9531897364676524\n"
     ]
    }
   ],
   "source": [
    "#transform test data\n",
    "\n",
    "pred_prob = nmf.transform(X_test)\n",
    "#print(pred_prob.shape)\n",
    "\n",
    "pred_labels = np.argmax(pred_prob, axis=1)\n",
    "\n",
    "\n",
    "print('acc:', accuracy_score(y_test, pred_labels))\n",
    "print('rand score', rand_score(y_test, pred_labels))\n",
    "print('fm score', fowlkes_mallows_score(y_test, pred_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e655871",
   "metadata": {
    "papermill": {
     "duration": 0.052629,
     "end_time": "2022-04-13T02:02:18.779378",
     "exception": false,
     "start_time": "2022-04-13T02:02:18.726749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### Latent Dirichlet Allocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3380899c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:18.879304Z",
     "iopub.status.busy": "2022-04-13T02:02:18.878545Z",
     "iopub.status.idle": "2022-04-13T02:02:23.253113Z",
     "shell.execute_reply": "2022-04-13T02:02:23.252475Z",
     "shell.execute_reply.started": "2022-04-13T01:07:42.521148Z"
    },
    "papermill": {
     "duration": 4.426182,
     "end_time": "2022-04-13T02:02:23.253275",
     "exception": false,
     "start_time": "2022-04-13T02:02:18.827093",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.36 s, sys: 1.96 ms, total: 4.37 s\n",
      "Wall time: 4.37 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LatentDirichletAllocation(n_components=5, random_state=10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Latent Dirichlet Allocation Model\n",
    "\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "lda = LatentDirichletAllocation(n_components=5, random_state=10)\n",
    "lda.fit(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "884362b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:23.355659Z",
     "iopub.status.busy": "2022-04-13T02:02:23.354968Z",
     "iopub.status.idle": "2022-04-13T02:02:23.426823Z",
     "shell.execute_reply": "2022-04-13T02:02:23.426145Z",
     "shell.execute_reply.started": "2022-04-13T01:07:46.931196Z"
    },
    "papermill": {
     "duration": 0.125972,
     "end_time": "2022-04-13T02:02:23.426982",
     "exception": false,
     "start_time": "2022-04-13T02:02:23.301010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.18791946308724833\n",
      "rand score 0.7112060199308522\n",
      "fm score 0.5811397573216798\n"
     ]
    }
   ],
   "source": [
    "pred_prob = lda.transform(X_test)\n",
    "\n",
    "\n",
    "pred_labels = np.argmax(pred_prob, axis=1)\n",
    "#print(pred_prob)\n",
    "#print(pred_labels)\n",
    "\n",
    "print('acc:', accuracy_score(y_test, pred_labels))\n",
    "print('rand score', rand_score(y_test, pred_labels))\n",
    "print('fm score', fowlkes_mallows_score(y_test, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74ba1756",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:23.527343Z",
     "iopub.status.busy": "2022-04-13T02:02:23.526594Z",
     "iopub.status.idle": "2022-04-13T02:02:31.995832Z",
     "shell.execute_reply": "2022-04-13T02:02:31.996545Z",
     "shell.execute_reply.started": "2022-04-13T01:07:47.007585Z"
    },
    "papermill": {
     "duration": 8.520998,
     "end_time": "2022-04-13T02:02:31.996740",
     "exception": false,
     "start_time": "2022-04-13T02:02:23.475742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.14429530201342283\n",
      "rand score 0.7842406164553816\n",
      "fm score 0.5421081133140944\n",
      "CPU times: user 19.7 s, sys: 13 s, total: 32.7 s\n",
      "Wall time: 8.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.decomposition import FactorAnalysis\n",
    "\n",
    "transformer = FactorAnalysis(n_components=5, random_state=0)\n",
    "transformer.fit(X_train.toarray()) \n",
    "#X_transformed.shape\n",
    "\n",
    "pred_prob = transformer.transform(X_test.toarray())\n",
    "\n",
    "pred_labels = np.argmax(pred_prob, axis=1)\n",
    "#print(pred_prob)\n",
    "pred_labels\n",
    "\n",
    "print('acc:', accuracy_score(y_test, pred_labels))\n",
    "print('rand score', rand_score(y_test, pred_labels))\n",
    "print('fm score', fowlkes_mallows_score(y_test, pred_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5d50fc",
   "metadata": {
    "papermill": {
     "duration": 0.058169,
     "end_time": "2022-04-13T02:02:32.144606",
     "exception": false,
     "start_time": "2022-04-13T02:02:32.086437",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### K mean clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "49c9b219",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:32.250665Z",
     "iopub.status.busy": "2022-04-13T02:02:32.249826Z",
     "iopub.status.idle": "2022-04-13T02:02:32.515179Z",
     "shell.execute_reply": "2022-04-13T02:02:32.515968Z",
     "shell.execute_reply.started": "2022-04-13T01:07:58.137549Z"
    },
    "papermill": {
     "duration": 0.32145,
     "end_time": "2022-04-13T02:02:32.516268",
     "exception": false,
     "start_time": "2022-04-13T02:02:32.194818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(298,)\n",
      "acc: 0.02348993288590604\n",
      "rand score 0.8912390120443812\n",
      "fm score 0.7395847783156247\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=0)\n",
    "kmeans.fit(X_test)\n",
    "pred_labels = kmeans.labels_\n",
    "print(pred_labels.shape)\n",
    "\n",
    "print('acc:', accuracy_score(y_test, pred_labels))\n",
    "print('rand score', rand_score(y_test, pred_labels))\n",
    "print('fm score', fowlkes_mallows_score(y_test, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "158d8713",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:32.623255Z",
     "iopub.status.busy": "2022-04-13T02:02:32.622493Z",
     "iopub.status.idle": "2022-04-13T02:02:32.964280Z",
     "shell.execute_reply": "2022-04-13T02:02:32.964833Z",
     "shell.execute_reply.started": "2022-04-13T01:07:58.454311Z"
    },
    "papermill": {
     "duration": 0.397839,
     "end_time": "2022-04-13T02:02:32.965015",
     "exception": false,
     "start_time": "2022-04-13T02:02:32.567176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rand score: 0.8912390120443812\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score, rand_score\n",
    "\n",
    "\n",
    "cfm = confusion_matrix(y_test, pred_labels)\n",
    "acc = accuracy_score(y_test, pred_labels)\n",
    "sns.heatmap(cfm, annot=True, linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(acc)\n",
    "plt.title(all_sample_title, size = 15);\n",
    "\n",
    "print('rand score:',rand_score(y_test,  pred_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2437fba5",
   "metadata": {
    "papermill": {
     "duration": 0.052215,
     "end_time": "2022-04-13T02:02:33.069388",
     "exception": false,
     "start_time": "2022-04-13T02:02:33.017173",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8abe5bd",
   "metadata": {
    "papermill": {
     "duration": 0.050291,
     "end_time": "2022-04-13T02:02:33.171338",
     "exception": false,
     "start_time": "2022-04-13T02:02:33.121047",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Step 3\n",
    "\n",
    "Compare with supervised learning\n",
    "\n",
    "1. \n",
    "    * logistic regression: train acc: 0.9966, test acc: 0.983  training time 1.75s\n",
    "    * suport vector machine, train acc: 1.0  ,test acc: 0.983 time: 6.1s\n",
    "    * random forest: train 0.985, test:0.96, time 4.5s\n",
    "    * Naive Bayes classifier for multinomial train 0.992, test 0.983, time 15ms\n",
    "\n",
    "2. \n",
    "    * Fix model to svc and try different data size.\n",
    "    * 10% test : train 1.0, test 1.0 acc\n",
    "    * 20% test size: train 1.0, test 0.98\n",
    "    * 50% test size: train 1.0 ,  test  0.98\n",
    "    * multinomial naive bayes is the most data efficeint, has lowest runtime and almost hightest score, and if set random forest max_depth and n_estimators to large will overfit.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0eec0338",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:33.279463Z",
     "iopub.status.busy": "2022-04-13T02:02:33.278733Z",
     "iopub.status.idle": "2022-04-13T02:02:36.430466Z",
     "shell.execute_reply": "2022-04-13T02:02:36.431346Z",
     "shell.execute_reply.started": "2022-04-13T01:57:24.503323Z"
    },
    "papermill": {
     "duration": 3.210083,
     "end_time": "2022-04-13T02:02:36.431600",
     "exception": false,
     "start_time": "2022-04-13T02:02:33.221517",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.97       171\n",
      "           1       0.98      1.00      0.99       130\n",
      "           2       0.99      0.95      0.97       142\n",
      "           3       0.99      1.00      0.99       164\n",
      "           4       0.96      0.96      0.96       138\n",
      "\n",
      "    accuracy                           0.98       745\n",
      "   macro avg       0.98      0.98      0.98       745\n",
      "weighted avg       0.98      0.98      0.98       745\n",
      "\n",
      "CPU times: user 3.09 s, sys: 7.11 ms, total: 3.1 s\n",
      "Wall time: 3.15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#logistic on TFIDF\n",
    "from sklearn.svm import NuSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    tfidf_vector, df['target'].values, test_size=0.5, random_state=42)\n",
    "\n",
    "#clf = linear_model.LogisticRegression()\n",
    "clf = SVC(kernel='rbf',gamma='scale', C=6)\n",
    "#clf = RandomForestClassifier(max_depth=10, n_estimators=1000,random_state=0)\n",
    "#clf = MultinomialNB()\n",
    "\n",
    "\n",
    "#scores = model_selection.cross_val_score(clf, X_train_tf, y_train, cv=3, scoring=\"accuracy\")\n",
    "#scores\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.score(X_train,y_train))\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "93718376",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T02:02:36.545913Z",
     "iopub.status.busy": "2022-04-13T02:02:36.545099Z",
     "iopub.status.idle": "2022-04-13T02:02:36.902113Z",
     "shell.execute_reply": "2022-04-13T02:02:36.902699Z",
     "shell.execute_reply.started": "2022-04-13T01:58:01.539825Z"
    },
    "papermill": {
     "duration": 0.416036,
     "end_time": "2022-04-13T02:02:36.902880",
     "exception": false,
     "start_time": "2022-04-13T02:02:36.486844",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rand score: 0.9820632171465685\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score, rand_score\n",
    "\n",
    "\n",
    "cfm = confusion_matrix(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "sns.heatmap(cfm, annot=True, linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(acc)\n",
    "plt.title(all_sample_title, size = 15);\n",
    "\n",
    "print('rand score:',rand_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35b49d95",
   "metadata": {
    "papermill": {
     "duration": 0.054461,
     "end_time": "2022-04-13T02:02:37.010959",
     "exception": false,
     "start_time": "2022-04-13T02:02:36.956498",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "de7032e1",
   "metadata": {
    "papermill": {
     "duration": 0.05281,
     "end_time": "2022-04-13T02:02:37.116811",
     "exception": false,
     "start_time": "2022-04-13T02:02:37.064001",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd5e5180",
   "metadata": {
    "papermill": {
     "duration": 0.052007,
     "end_time": "2022-04-13T02:02:37.221724",
     "exception": false,
     "start_time": "2022-04-13T02:02:37.169717",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Step 4\n",
    "\n",
    "Submission and Organized Jupyter Notebook Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c0ef030",
   "metadata": {
    "papermill": {
     "duration": 0.053943,
     "end_time": "2022-04-13T02:02:37.329525",
     "exception": false,
     "start_time": "2022-04-13T02:02:37.275582",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Github link for the nodebook: https://github.com/carsonxie/Machine-Learning-Projects/tree/main/NLP_News%20Classification%20Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b964c6e7",
   "metadata": {
    "papermill": {
     "duration": 0.052522,
     "end_time": "2022-04-13T02:02:37.434953",
     "exception": false,
     "start_time": "2022-04-13T02:02:37.382431",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "44027939",
   "metadata": {
    "papermill": {
     "duration": 0.052284,
     "end_time": "2022-04-13T02:02:37.539995",
     "exception": false,
     "start_time": "2022-04-13T02:02:37.487711",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Reference: \n",
    "\n",
    "https://towardsdatascience.com/nmf-a-visual-explainer-and-python-implementation-7ecdd73491f8\n",
    "\n",
    "https://www.kaggle.com/code/thebrownviking20/topic-modelling-with-spacy-and-scikit-learn\n",
    "\n",
    "https://www.kaggle.com/code/aryankaul31/aryan-bbc-news-classification"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 49.565967,
   "end_time": "2022-04-13T02:02:38.504618",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-04-13T02:01:48.938651",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
